Engineering Pod Lead

About Turing -
Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Job Overview

An  Engineering Pod Lead responsible for managing a small team creating simulation-based design problems used to train and evaluate advanced AI models. The role owns problem quality, reproducible environments, and coordination with AI researchers, and requires strong EE domain depth plus hands-on Python, Docker, and basic cloud skills to review and support the pod end-to-end.

Key Responsibilities

• Team Leadership
  • Lead and manage a pod of 4–8 Engineering Design Specialists by assigning tasks, reviewing deliverables, removing blockers, and mentoring on problem design, simulation rigor, and documentation.
  •  Act as the main point of contact with AI research teams to translate model feedback into refinements, while running regular syncs, tracking progress, and reporting updates and risks to program leads.
• Technical Oversight
  • Oversee and approve pod outputs, maintain reproducible Docker-based simulation environments, support cloud infrastructure for simulation/grading, 
  • Develop and review Python automation/validation, and proactively resolve dependency, versioning, and reproducibility issues.
• Problem Design (Individual Contribution)
  • Personally design a portfolio of high-difficulty electrical engineering problems in your chosen subdomain.
  • Set the quality bar and design philosophy for the pod through your own example submissions.

Engineering Subdomains

You must have deep expertise in at least one of the following, with working familiarity across others being a strong plus:

• Analog / Mixed-Signal IC Design — op-amp topologies, ADC/DAC design, noise analysis, layout-aware design, SPICE-level simulation.
• Power Electronics — DC-DC converters, inverter design, magnetics sizing, thermal management, efficiency optimization.
• RF / Microwave Engineering — filter design, impedance matching, S-parameter analysis, transmission line and antenna design.
• Digital Systems / FPGA / ASIC — RTL design, timing closure, synthesis-aware design, hardware-software co-design.
• Embedded Systems — microcontroller/DSP-based design, real-time constraints, peripheral interfacing, power budgeting.

Required Qualifications

• Master's degree or PhD in Electrical Engineering o Mechanical Engineer  a closely related discipline.
• At least 5 years of hands-on electrical engineering or mechanical engineering design experience, with a track record of owning designs from specification through validated implementation.
• At least 1–2 years of experience in a technical lead, senior engineer, or team coordination role.
• Proficiency with Python for scripting, automation, and simulation workflows.
• Hands-on experience with Docker: building images, writing Dockerfiles, managing containerized environments.
• Familiarity with at least one cloud platform (AWS, GCP, or Azure) — comfortable running workloads, managing storage, and monitoring jobs.
• Proficiency with at least one open-source EE simulation tool (e.g., ngspice, PySpice, Qucs, OpenEMS, scikit-rf, or similar).

Nice-to-Have

• Experience designing engineering coursework, exam problems, or design challenges at graduate level.
• Familiarity with CI/CD pipelines or automated testing frameworks.
• Exposure to LLM evaluation, AI benchmarking, or training data curation.
• Cross-domain familiarity with Control Systems, Embedded/Robotics, or Power Systems.

Evaluation Process -

• Shortlisted candidates will be sent a Job Interest Form.
• Shortlisted candidates will receive a Job Interest Form. After the initial profile review, we will share an assessment, which must be completed within 24 hours. Based on the assessment submission and your responses, we may schedule an interview round.
• Based on the assessment outcomes, shortlisted candidates will be contacted to discuss the pre‑onboarding requirements.